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Assessing practical significance of the proportional odds assumption

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  • Kim, Ji-Hyun

Abstract

The proportional odds model is a popular regression model for ordinal categorical responses, which has a rather strong underlying assumption, the proportional odds assumption. The rejection of the null assumption, however, is not very informative since a statistical significance does not necessarily imply a practical significance. This paper proposes a graphical method for assessing the practical significance of the assumption.

Suggested Citation

  • Kim, Ji-Hyun, 2003. "Assessing practical significance of the proportional odds assumption," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 233-239, November.
    • Handle: RePEc:eee:stapro:v:65:y:2003:i:3:p:233-239
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    Citations

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    Cited by:

    1. Aguilar, Francisco X. & Daniel, Marissa “Jo” & Cai, Zhen, 2014. "Family-forest Owners’ Willingness to Harvest Sawlogs and Woody Biomass: The Effect of Price on Social Availability," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 0, pages 1-21.
    2. Valerii Baidin & Christopher J. Gerry & Maria Kaneva, 2021. "How Self-Rated is Self-Rated Health? Exploring the Role of Individual and Institutional Factors in Reporting Heterogeneity in Russia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 675-696, June.
    3. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.
    4. Peter van der Zwan & Roy Thurik & Isabel Grilo, 2010. "The entrepreneurial ladder and its determinants," Applied Economics, Taylor & Francis Journals, vol. 42(17), pages 2183-2191.
    5. Gerhard Tutz & Moritz Berger, 2022. "Sparser Ordinal Regression Models Based on Parametric and Additive Location‐Shift Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 306-327, August.
    6. Woo, C.K. & Zarnikau, J. & Moore, J. & Horowitz, I., 2011. "Wind generation and zonal-market price divergence: Evidence from Texas," Energy Policy, Elsevier, vol. 39(7), pages 3928-3938, July.
    7. Anasua Chakraborty & Hichem Omrani & Jacques Teller, 2022. "A Comparative Analysis of Drivers Impacting Urban Densification for Cross Regional Scenarios in Brussels Metropolitan Area," Land, MDPI, vol. 11(12), pages 1-20, December.
    8. Tutz, G. & Berger, M., 2017. "Separating location and dispersion in ordinal regression models," Econometrics and Statistics, Elsevier, vol. 2(C), pages 131-148.
    9. Arthur Jacobs & Elsy Verhofstadt & Luc Van Ootegem, 2023. "Are more automatable jobs less satisfying?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1059, Ghent University, Faculty of Economics and Business Administration.
    10. Maria Iannario & Domenico Piccolo, 2016. "A comprehensive framework of regression models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 233-252, August.

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